Analysis of Shanghai Composite Index Variation Based on Regression Analysis
In this paper, through collecting data of Shanghai Composite Index since 2007, we analyze overall trend of the Shanghai stock market after the financial crisis, and carry on the forecast to the future trend in order to provide a meaningful guidance for people’s investment securities. Because the fitting results of simple regression is not good, we consider the long-term trend, seasonal fluctuations, cyclical fluctuations, irregular variables and other factors. We also add lagged variables and establish an ARIMA model through SPSS statistical analysis software. The fitting degree of model we built is good and the effect of prediction is significant improvement in the analysis.
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Dai, W. S., Shao,Y. J. E., & Lu, C. J. (2013). Incorporating feature selection method into support vector regression for stock index forecasting. Neural Computing & Applications, 23(6), 1551-1561.
Mezali, H., & Beasley, J. E. (2013). Quantile regression for index tracking and enhanced indexation. Journal of the Operational Research Society, 64(11), 1676-1692.
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